This invention relates to the automated characterization and identification of objects, including automated detection of their borders, in intravascular ultrasonic imaging.
The value of ultrasonic imaging can be enhanced if models can be developed which accurately correlate properties of-ultrasound objects in an in-vivo environment. Heretofore there have been few automated approaches in the field of in-vivo ultrasonic object definition and identification. Previously proposed approaches may be classified in two categories. First, the defining of an object as an area surrounded by a detected border. Detection of the border in turn is based on local properties and behavior of the border. Second, the development of a theoretical model for an ultrasound object which is validated for in vitro studies.
According to the first category, approaches have been developed at the Thoraxcenter in Rotterdam, Holland, and at the University of Iowa which employ feature extraction techniques for border detection. In those approaches an object is defined as the area encompassed by a detected border, and the algorithms used are optimized to provide the best possible border. These approaches are limited because algorithms provide little information about the parameters characterizing the object under observation. Neither can the algorithms adapt their behavior in accordance with frame-to-frame variants in object properties. In addition, the algorithms are computational and time intensive in cross-sectional area computation, since they must completely calculate the object border in each frame of the volume.
In the second category of approaches, tissue modeling techniques have been developed for comparing data patterns with predefined models, e.g., at the Stanford Center for Cardiac Interventions and the University of Texas. In these types of techniques, a consistent tissue behavior is assumed which can be modeled. The models describe internal properties of an object which can be used to identify the object. However, such models are inherently limited in that by their nature they cannot accommodate variations in object properties from patient to patient, or even from frame to frame. A paper by Petropulu et al. entitled MODELING THE ULTRASOUND BACKSCATTERED SIGNAL USING α-STABLE DISTRIBUTIONS, 1996 IEEE Ultrasonics Symposium, p. 103 is representative of the model-based approach. Therein certain assumptions about theoretical statistical behavior are made, and the assumptions are used to identify the object in an in-vivo case study. This limited approach is subject to significant errors because it yields a model which only partially describes the object behavior and does not take into account variations from case to case.
Most known techniques for object border detection use a purely manual method for border tracing, which is done simply by drawing the boundary of the object. This procedure is slow and is subject to errors and variations between users. Moreover, it does not allow for the characterization of the object within the border.
One known description of a combination of different approaches is Spencer et al., CHARACTERISATION OF ATHEROSCLEROTIC PLAQUE BY SPECTRAL ANALYSIS OF 30 MHZ INTRAVASCULAR ULTRASOUND RADIO FREQUENCY DATA, 1996 IEEE ULTRASONICS SYMPOSIUM, p. 1073, wherein a statistical model is developed from in-vitro studies, then applied to in-vivo cases. Such an approach is limited by both the differences between in-vitro and in-vivo conditions and between in-vivo cases.
What are needed are better techniques for border detection and for identifying and characterizing objects and features of ultrasonic imaging.